The Experiential Learning Pathway of Cancer Survivors as They Recover Their Lives Post-Treatment: A Qualitative Study
Bibliographic record
Abstract
For many cancer survivors, post-treatment challenges are predominantly related to their personal and social lives. These challenges are part of an experiential learning process linked to a survivor's identity, their desire to preserve independence, their social roles, and responsibilities along with a return to their normal lives. We used interpretive description to describe the experiential learning process of cancer survivors as they recover post-treatment. Data from five group discussions with 27 participants were combined with data from 9 in-depth individual interviews that examined post-treatment challenges. Through an iterative qualitative analysis, we uncovered 3 experiential learning pathways. Narrative vignettes are used to portray and highlight learning involved in accepting loss, asking for help, and rebuilding authentic social networks. Experiential learning shares recognizable features among individuals identified as milestones. These lead to a greater understanding of how cancer survivors acquire a new sense of self and recover their lives post-treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".